Objective: The approximate life span of a silicone maxillofacial prosthesis is as short as1.5–2 years of clinical service, then a new prosthesis should be fabricated. The most common reasonfor re-making the prosthesis is silicone mechanical properties degradation. The aim of this studywas to assess some mechanical properties of VST-30 silicone for maxillofacial prostheses after addi-tion of intrinsic pigments.Methods: Two types of intrinsic pigments (rayon flocking and burnt sienna); each of them wasincorporated into silicone. One hundred and twenty samples were prepared and split into 4 groupsaccording to the conducted tests (tear strength, hardness, surface roughness, and tensile strengthand elongation percentage) with 30 samples for ea

Aim: To learn more about Oral Lichen Planus Iraqi patients, including their background information, symptoms, and prognosis. Materials and Methods: From the Oral and Maxillofacial Pathology Department, College of Dentistry, Baghdad University, we retrospectively reviewed the medical records of 68 patients with a histologically confirmed clinical diagnosis of oral lichen planus and subsequently contacted the patients by phone to evaluate their prognosis. Results: Females were more likely than males to experience severe pain; the reticular form of Oral Lichen Planus was the most prevalent at 38.2%, but the erosive type was more prevalent among females. Only 53 of 68 patients responded to phone calls. More than 37% of those respondents reporte

The new bidentate Schiff base ligand namely [(E)-N1-(4-methoxy benzylidene) benzene-1, 2-diamine] was prepared from condensation of 4-Methoxy benzaldehyde with O-Phenylene diamine at 1:1 molar ratio in ethanol as a solvent in presence of drops of 48% HBr. The structure of ligand (L) was characterized by, FT-IR, U.V-Vis., 1H-, 13C- NMR spectrophotometer, melting point and elemental microanalysis C.H.N. Metal complexes of the ligand (L) in general molecular formula [M(L)3], where M= Mn(II), Co(II), Ni(II),Cu(II) and Hg(II); L=(C14H14N2O) in ratio (1:3)(Metal:Ligand) were synthesized and characterized by Atomic absorption, FT- IR, U.V-Vis. spectra, molar conductivity, chloride content, melting point and magnetic susceptibility from the above d

Multiple sclerosis (MS) is a chronic, inflammatory demyelinating disease of central nervous system with complex etiopathogenesis that impacts young adults (Lee et al., 2015), and MS impacts younger and middle aged character and leads to a range of disabilities that can alter their daily routines (Yara et al, 2010). Although, the exact cause of MS is still undetermined, the disease is mediated by adaptive immunity through the infiltration of T cells into the central nervous system (Bjelobaba et al, 2017). MS causes the Focal neurological symptomsand biochemical changes in the molecular level and the variation of neural cells such as loss or alteration of sensation, motor function, visible signs such as blurred vision or transient blindness,

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class